Speaker
Description
Panchromatic surveys such as COSMOS have expanded our understanding of how galaxies evolve through cosmic time immensely. Surveys such as the Vera C. Rubin Observatory's LSST are promising an unprecedented view of this evolutionary paradigm provided the massive data challenge they pose is answered. We have been developing pop-cosmos, a comprehensive galaxy population model housing a 16-parameter SPS model parametrized by a flexible diffusion model. The 16D distribution over galaxy properties including their redshift is forward-calibrated on 26-band photometry from COSMOS with a deep infrared selection. In a recent paper we used a galaxy catalogue drawn from the trained pop-cosmos model to investigate the stellar mass assembly and star formation histories of galaxies up to z=4. I will briefly summarize key results such as the cosmic star formation rate density inferred from our model, and will present a look into the quenching mechanisms of galaxy populations. Our investigation finds a shift of the cosmic dawn towards earlier lookback times, and uncovers correlations between star formation, AGN activity and quenching difficult to capture without a population-level analysis. In this talk I will mainly focus on our work expanding the forward process of pop-cosmos to jointly train on the morphology of galaxies and their photometry from profile fitting. The morphology of galaxies encodes important information about their evolutionary stage, and I will talk about how the inclusion of this dimension in the forward modelling impacts the population model. I will describe how we connect the morphology information using the compressed latent representation learned by an encoder-decoder network, like a convolutional variational autoencoder, effectively keeping the computational cost increase due to this new mode to a minimum. To conclude, I will present how this approach unlocks a new avenue for population-level causal inference in astrophysics, using the causal relation between quenching and morphological transformation as the primary example.